We propose and experimental demonstrate a Dynamic Threshold Identification Method (DTIM) based on the Raman distributed fiber sensor (RDFS) system for detecting the pipeline leak. The DTIM can detect the position of pipeline leak according to distributed temperature measured by the RDFS. The DTIM includes three steps, which are the space segmentation detection, $3\sigma $ criterion and identification of historical data. The experimental results show that the proposed RDFS can detect and accurately locate the leak position by using the DTIM, and the positioning accuracy is 1 m. In addition, the temperature change rate of the leak position performs a quadratic function relationship with the leak rate, and the leak rate can be estimated by this method. It provides an effective distributed monitoring method for the leak monitoring of pipeline.

Pipeline Leak Detection Using Raman Distributed Fiber Sensor With Dynamic Threshold Identification Method

Raman-based distributed temperature sensing (RDTS) can achieve temperature measurement of tens of kilometers and is widely used in temperature monitoring of crucial facilities. The accuracy of RDTS is highly determined by the signal-to-noise ratio (SNR) of the acquired spontaneous Raman scattering (SpRS) signals, which is about 60 dB weaker than the pump pulse. Therefore, the performance of single-mode fiber (SMF) based conventional RDTS is poor. To improve its performance, many methods have been proposed, including the use of special optical fibers, pulse coding techniques, and denoising algorithms. However, these methods have their limitations. Here, we propose and experimentally demonstrate a deep 1-D denoising convolutional neural network (1DDCNN) to enhance the performance of RDTS. A simplified RDTS model is built to train and optimize the 1DDCNN. To verify the performance of the 1DDCNN on actual data, we experimentally measure the SpRS data of a 10-km SMF with an average time of 1 s and a spatial resolution of 3 m. The well-trained 1DDCNN reduces the temperature uncertainty from 6.4 °C to 0.7 °C with high fidelity. As a comparison, the commonly used wavelet denoising algorithm can only reduce it to 3.7 °C. Besides, the 1DDCNN does not require manual parameter adjustment and is not affected by the sampling rate, giving it significant advantages in practical applications compared with conventional denoising algorithms. Moreover, we believe that the proposed 1DDCNN can be applied to other distributed optical fiber sensing systems by fine-tuning the network with appropriate training data.

High-Performance Raman Distributed Temperature Sensing Powered by Deep Learning

Abstract Raman distributed optical fiber sensing has been demonstrated to be a mature and versatile scheme that presents great flexibility and effectivity for the distributed temperature measurement of a wide range of engineering applications over other established techniques. The past decades have witnessed its rapid development and extensive applicability ranging from scientific researches to industrial manufacturing. However, there are four theoretical or technical bottlenecks in traditional Raman distributed optical fiber sensing: (i) The difference in the Raman optical attenuation, a low signal-to-noise ratio (SNR) of the system and the fixed error of the Raman demodulation equation restrict the temperature measurement accuracy of the system. {ii) The sensing distance and spatial resolution cannot be reconciled. (iii) There is a contradiction between the SNR and measurement time of the system. (iv) Raman distributed optical fiber sensing cannot perform dual-parameter detection. Based on the above theoretical and technical bottlenecks, advances in performance enhancements and typical applications of Raman distributed optical fiber sensing are reviewed in this paper. Integration of this optical system technology with knowledge based, that is, demodulation technology etc. can further the performance and accuracy of these systems. 

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Physics and applications of Raman distributed optical fiber sensing

A high-sensitivity surface plasmon resonance (SPR) sensor is designed and analyzed numerically. The sensor is constructed on the eightfold U-shaped photonic quasi-crystal fiber (PQF) and coated with indium tin oxide (ITO). The coupling between the core mode and surface plasmon polariton mode is enhanced due to shortening of the distance between the core and the ITO layer, so that the PQF-SPR sensor exhibits high refractive index (RI) sensitivity in the near-infrared region. The maximum wavelength sensitivity and the corresponding resolution of this sensor are 42,000 nm/RIU and 2.38×10−6RIU, respectively. The average wavelength sensitivity is 12,750 nm/RIU in the refractive index range of 1.306–1.386. This advanced sensor is suitable for the determination of RIs in the near-infrared region.

Surface plasmon resonance sensor based on U-shaped photonic quasi-crystal fiber

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In the temperature monitoring field, the Raman Distributed Temperature Sensor (R-DTS) is required with a temperature accuracy better than 1 °C over a long distance. This paper proposes and experimentally demonstrates an R-DTS system based on the difference sensitive-temperature compensation to optimize the temperature accuracy with the enhanced temperature sensitivity of backscattered spontaneous Raman scattering. While operating in the experiment, the distributed temperature measurement and theory analysis use the dual-demodulation, self-demodulation and double-end configuration principles for R-DTS are demonstrated. The experimental results show that the temperature accuracy is 12.54 °C, 8.53 °C and 15.00 °C along the 10.8 km under the standard R-DTS systems, respectively. Further, we analyze and recalibrate the intensity of the Raman scattering signal in theory, and substitute the sensitive-temperature factor (M(L)) into the Raman scattering signal. Finally, a novel temperature demodulation method with difference sensitive-temperature compensation is applied to the dual-demodulation, self-demodulation, and double-end configuration R-DTS systems. After compensation, the temperature accuracy can be optimized to 0.38 °C, 0.36 °C and 0.56 °C at the same position. It proves that the proposed method can make the temperature accuracy better than 1 °C for these three demodulation systems.

High-accuracy distributed temperature measurement using difference sensitive-temperature compensation for Raman-based optical fiber sensing.

A novel Rayleigh noise suppression method is proposed to improve temperature accuracy and resolution for Raman distributed fiber-optics sensors. The proposed temperature demodulation method can eliminate temperature measurement inaccuracy caused by Rayleigh noise. The experimental results indicate that the temperature accuracy is optimized from 6.2°C to 1.7°C at a sensing distance of 9.1 km by using the proposed method, and the temperature resolution leads to about 1.5°C improvement compared with the tradition demodulation method at a sensing distance of 10.0 km. The proposed method provides a robust and reliable high performance for long sensing ranges.

Temperature accuracy and resolution improvement for a Raman distributed fiber-optics sensor by using the Rayleigh noise suppression method.

Solar cells can operate at a lower efficiency after a certain temperature, which is caused by a negative thermal coefficient. Therefore, the temperature prediction of photovoltaic (PV) modules is critical to accurately evaluate the efficiency of photovoltaic devices. We propose and experimentally demonstrate a Fuzzy Temperature Difference Threshold Method (FTDTM) based on Raman Distributed Temperature Sensor (RDTS) system for the detection and prediction of PV module temperature. The FTDTM consists of four steps, i.e., division of the universe, establishment of fuzzy relationships, definition of relationship matrix and calculation of predicted temperature. The experimental results show that the proposed RDTS can detect and accurately predict temperature trends by using the FTDTM. When the system window base of the FTDTM is set to 2, the average absolute error of the predicted temperature is 1.08 °C, and the fluctuation range of prediction error is ±3.7 °C. In addition, the experiment studied some factors affecting temperature distribution characteristics of PV modules, including solar radiation intensity, surface dust and inclination angle. We provide a solution for large-scale PV module temperature detection and early warning through RDTS systems with FTDTM.

Yang Xu

Papers

Pipeline Leak Detection Using Raman Distributed Fiber Sensor With Dynamic Threshold Identification Method

High-accuracy distributed temperature measurement using difference sensitive-temperature compensation for Raman-based optical fiber sensing.

Temperature accuracy and resolution improvement for a Raman distributed fiber-optics sensor by using the Rayleigh noise suppression method.

Photovoltaic Panel Temperature Monitoring and Prediction by Raman Distributed Temperature Sensor With Fuzzy Temperature Difference Threshold Method

Reconstruction Compression Correlation Demodulation for Raman Optical Time Domain Reflection